Perancangan Website Analisis Segmentasi Perilaku dan Minat Berlangganan Spotify Premium menggunakan Metode K-Medoids dan Regresi Polinomial
DOI:
https://doi.org/10.55606/juisik.v6i2.2431Keywords:
Customer Segmentation, K-Medoids, Polynomial Regression, Silhouette Score, Spotify PremiumAbstract
This study aims to analyze customer behavior segmentation and subscription interest in Spotify Premium using a data mining approach. The problem addressed is the limited understanding of user behavior influencing subscription decisions, resulting in less optimal strategies for increasing premium subscribers. The methods used include the K-Medoids algorithm for customer clustering and polynomial regression to analyze relationships between behavioral variables. The dataset used is the Spotify User Behaviour Dataset, which contains various user behavior attributes. The research stages include data pre-processing, determining the optimal number of clusters using the Elbow method, clustering, and regression analysis. The results show that the optimal number of clusters is k=9, allowing customers to be grouped into nine segments based on similar behavior patterns, with a Silhouette Score of 0.1659 indicating weak to moderate cluster quality. Furthermore, the polynomial regression results show a Mean Squared Error (MSE) value of 1.0694 and a coefficient of determination (R2) of -0.1109, indicating that the model has not optimally explained the data variability. However, factors such as usage intensity and content preferences still influence user’s interest in subscribing. This study provides insights into customer characteristics and support more targeted strategies to increase premium subscriptions.
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